Engineers, computer scientists and technicians alike have affinity with mathematics, numbers, formulas. They are the kind of people who prefer to know How it works than What it delivers. They are the ones who disassemble an old watch and try to fix it.

One of my common mistakes in my first years in the corporate world was that I faced it like it was a college examination: I tried to explain to the executives the hypothesis, the developments, the formulas, and expected then to ask about some technical stuff, like why I used a Chi-Squared distribution instead of a Normal one, as if they were academic teachers.

Surely it was not the case. In the corporate world, and in the real world in general, people want answers. Just it, answers. And they want you to explain the solution in simple, easy-to-follow and reasonable steps. They are not stupid and of course they don’t want to be treated like that. They will check the big numbers and look for contradictions. And they want to know, in human words, why is your solution the correct one. You have to tell a history. It is your task to explain what you mean in simple ways, and to defend your position using the same domain of words (not formulas).

The optimal solution for the wrong problem

In the same line of thinking, I spent a lot of years in my quest to always find the optimal solution with the best up-to-date method for analytical problems.

Wrong. Completely wrong.

An optimal solution for the wrong problem solves nothing.

People in the real world don’t need the optimal solution. They need a solution that works effectively. And the only way to find an effective solution is in the Questions, not in the method of resolution of the model. The problem is in the formulation of the problem.

If you want to solve a real world problem, it will not come with a formulation “Use integer programming to solve this set of equations”. It will come in diffuse information pieces, with dozens of people telling different points of will, with spurious sparse databases. And you will have to formulate the questions.

The model can be as simple as a single linear equation, as long as it provides the effective solution.

Challenges of Analytics

I see a lot of excitement and buzz words: Big Data, Artificial intelligence, Watson, R-Phyton or whatever.

These are only tools. They don’t solve problems alone. They will not solve all the problems of the world.

In a couple of years, these buzz words will fade, some will be forgotten, and new buzz names will appear, promising the same things.

For me, better than trying to provide the magical solution, is to make the basics. Simple and effective solutions. And these solutions must:

Provide information, guidance, clarity

Support decision

Empower analysis

That is, provide ways to make questions.

Arnaldo Gunzi

Feb 2016

“The release of atom power has changed everything except our way of thinking. The solution to this problem lies in the heart of mankind. If only I had known, I would have become a watchmaker”

Great post! I have seen many of my colleages concentrating too much on data, not the REAL BUSINESS problem. In my opinion, as data analysts, we should treat data analysis as a PRODUCT. The process of data analysis is the process to create a product that meets the market needs. Therefore, it should accompanied with the concept of “Lean startup” with “MVP” 🙂 It is the reason I find your blog post very interesting! Great post and keep posting!

Great post! I have seen many of my colleages concentrating too much on data, not the REAL BUSINESS problem. In my opinion, as data analysts, we should treat data analysis as a PRODUCT. The process of data analysis is the process to create a product that meets the market needs. Therefore, it should accompanied with the concept of “Lean startup” with “MVP” 🙂 It is the reason I find your blog post very interesting! Great post and keep posting!

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